1. Field of the Invention
The present invention relates to a method and an apparatus for binarizing an image signal based on a randomization error diffusion process, and more particularly to a method and an apparatus for binarizing an image signal based on weighting randomization patterns in the conversion process.
2. Description of the Related Art
For reproducing a gradation image on a display unit or a printer which is capable of displaying only binary representations, a multi-valued image signal is converted into a binary image signal made up of only 0s and 1s. There is a known error diffusion process for binarizing a multi-valued image signal.
According to the error diffusion process, when a multi-valued image signal representing an input pixel is converted into a binary image signal by comparison with a threshold signal, an error produced by the binarization is distributed and added to pixels in the vicinity of the input pixel, and resultant multi-valued image signals are successively binarized as new multi-valued image signals representing those pixels.
If it is assumed that a multi-valued image signal representing an input pixel (x,y) is represented by I(x,y) (x indicates the position in a main scanning direction and y indicates the position in an auxiliary scanning direction) and a binary image signal converted from the multi-valued image signal is represented by P(x,y), then a binarization error signal E(x,y) is expressed by: EQU E(x,y)=I(x,y)-P(x,y) (1)
The binarization error signal E(x,y) determined according to the equation (1) is diffused by being added to multi-valued image signals I(x-k,y-l) (k,l are 0 or .+-.1) representing eight pixels around the pixel (x,y) under consideration, according to the equations (2): EQU I(x+1,y)=I(x+1,y)+E(x,y).times.A, EQU I(x-1,y+1)=I(x-1,y+1)+E(x,y).times.B, EQU I(x,y+1)=I(x,y+1)+E(x,y).times.C, EQU I(x+1,y+1)=I(x+1,y+1)+E(x,y).times.D (2)
where A, B, C, D are error diffusion coefficients for diffusing the binarization error signal E(x,y) at certain ratios. While only four of the eight pixels around the pixel (x,y) under consideration are involved in the equations (2), the same calculations are also effected on the other four pixels that are positioned in point-symmetry relation to the above four pixels with respect to the pixel (x,y). The resultant multi-valued image signals I(x-k,y-l) are then converted into respective binary image signals P(x-k,y-l) by comparison with a given threshold signal. The above calculating procedure is repeated until a certain finishing condition is satisfied.
The equations (2) are mathematically equivalent to the following equation (2'): ##EQU1## where W(k,l) represents an error diffusion coefficient which is indicated by A, B, C, D in the equations (2).
The above error diffusion process for converting multi-valued image signals into binary image signals is effective in reproducing continuous gradation signals representing halftone-dot images or photographic images while suppressing the generation of moire patterns. However, the error diffusion process is disadvantageous in that it allows an undesirable texture (fine periodical pattern), such as a striped pattern peculiar to a binarized image or a matted area in a region of certain density level, to be generated in a binarized image depending on the manner in which the error diffusion coefficients A, B, C, D are established.
To avoid the above shortcomings, there has been proposed a process (hereinafter referred to as a "randomization error diffusion process") of changing the error diffusion coefficients A, B, C, D at random using random numbers and applying the changed error diffusion coefficients A, B, C, D to the binarization error signal E(x,y) to reproduce an image that is less susceptible to an undesirable texture. For details, see the Journal of Image Electronics Society, Vol. 20, No. 5, 1991, pages 443-449.
The proposed randomization error diffusion process has a problem in that since the image signals of pixels in regions free from an undesirable texture, e.g., in low- and high-density regions, are also processed with random numbers, the coarseness of the image increases in these regions.